Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add K-Dense-AI/drug-discovery-agent-skills --skill protein-binder-designgit clone --depth 1 https://github.com/K-Dense-AI/drug-discovery-agent-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/k-dense-ai/drug-discovery-agent-skills/protein-binder-design)<a href="https://agentmods.dev/skills/k-dense-ai/drug-discovery-agent-skills/protein-binder-design"><img src="https://agentmods.dev/badge/skills/k-dense-ai/drug-discovery-agent-skills/protein-binder-design/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/k-dense-ai/drug-discovery-agent-skills/protein-binder-design"><img src="https://agentmods.dev/badge/skills/k-dense-ai/drug-discovery-agent-skills/protein-binder-design.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium analysis-evasion · line 1 Suspicious Unicode normalization or mixed-script contentFix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00144 | $0.01977 |
| Opus 5 | $0.00072 | $0.00988 |
| Sonnet 5 | $0.00029 | $0.00395 |
| Haiku 4.5 | $0.00014 | $0.00198 |
Grade A, and why
protein-binder-design scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 164 lines — stays where its author put it; the contents beside it link to each section on GitHub.
De Novo Protein Binder Design
Designing a new protein that binds a chosen surface used to be a research project. BindCraft reports 10–100% experimental success without high-throughput screening, and the pipeline is open source. The hard part is no longer the algorithm — it is choosing where to bind, and knowing that the metrics which select designs cannot tell you which one works.
Tools: BindCraft 1.5+ (Nature 2025, MIT), or RFdiffusion + ProteinMPNN + AlphaFold2. Both need AlphaFold2 weights and an NVIDIA GPU; a single trajectory is roughly half an hour. The bundled scripts prepare targets and filter output, and run anywhere.
Read references/epitope-selection.md before anything else, references/bindcraft-and-rfdiffusion.md to choose a pipeline, and references/filtering-and-validation.md before ordering — that one is judgement, not syntax.
The three scripts
| Script | Answers |
|---|---|
binder_target_spec.py |
Where should the binder bind, and is that site usable? |
design_manifest.py |
Which pipeline, how many trajectories, what will it cost? |
binder_filter.py |
Which designs survive, and which should I actually order? |
The epitope decides the campaign
Everything downstream is compute spent on this one choice, and a bad site produces designs that fold beautifully and bind nothing — with no signal that the site was the problem.
python skills/protein-binder-design/scripts/binder_target_spec.py hotspots \
--pdb target.pdb --chain A --hotspots 45,47,52,89
# 4 hotspot residues, maximum separation 14.2 A
resseq resname neighbours exposure issue
45 TYR 16 surface
47 LEU 24 buried buried -- cannot be contacted
Three checks it applies: hotspots must be surface-exposed (a buried residue cannot be contacted, and neither design tool will say so), there should be 3–6 of them, and they must sit within ~25 Å — a wider spread is asking a single binder to do something impossible.
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 164 lines · 144 tokens per session scan A bfb45e7aacc8
protein-binder-design is a skill published in the GitHub repository K-Dense-AI/drug-discovery-agent-skills (28 stars, last pushed 5d ago), licensed MIT. It adds 144 tokens to every session and 1,977 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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